Correcting for base-population differences and unknown parent groups in single-step genomic predictions of Norwegian Red cattle

Bias and inflation in genomic evaluation with the single-step methods have been reported in several studies. Incompatibility between the base-populations of the pedigree-based and the genomic relationship matrix (G) could be a reason for these biases. Inappropriate ways of accounting for missing parents could be another reason for biases in genetic evaluations with or without genomic information. To handle these problems, we fitted and evaluated a fixed covariate (J) that contains ones for genotyped animals and zeros for unrelated non-genotyped animals, or pedigree-based regression coefficients for related non-genotyped animals. We also evaluated alternative ways of fitting the J covariate together with genetic groups on biases and stability of breeding value estimates, and of including it into G as a random effect. In a whole vs. partial data set comparison, four scenarios were investigated for the partial data: genotypes missing, phenotypes missing, both genotypes and phenotypes missing, and pedigree missing. Fitting J either as fixed or random reduced level-bias and inflation and increased stability of genomic predictions as compared to the basic model where neither J nor genetic groups were fitted. In most models, genomic predictions were largely biased for scenarios with missing genotype and phenotype information. The biases were reduced for models which combined group and J effects. Models with these corrected group covariates performed better than the recently published model where genetic groups were encapsulated and fitted as random via the Quaas and Pollak transformation. In our Norwegian Red cattle data, a model which combined group and J regression coefficients was preferred because it showed least bias and highest stability of genomic predictions across the scenarios. Towards an unbiased and stable combination of information from genotyped and non-genotyped animals in genomic prediction models. Lay Summary Our study dealt with strategies on how to reduce biases (inflation and level-bias) and improve a parameter related to accuracy (stability) of genomic predictions of breeding values that combine genotyped and non-genotyped animals, which are denoted as single-step genomic predictions. We tried to remedy incompatibilities between the pedigree- and the genomics-based relationships matrices by fitting a covariate (J) that corrects for base-population differences that may occur between both relationship matrices. We also evaluated alternative ways to combine the J covariate and genetic group effects to account for missing parental information, which often occurs in practical breeding schemes. We found that fitting J either as fixed or random reduced level-bias and inflation and increased stability of genomic predictions as compared to the basic model where neither J nor genetic groups were fitted. Level-biases and inflation of breeding value estimates were reduced, and stability of genomic predictions improved for models which combined group and J effects. A model which fits group regression coefficients minus the part that could be explained from pedigree was recommended because it showed least bias and highest stability across the scenarios and has theoretical justification.

Automated summary

This study focused on reducing biases and improving stability of genomic predictions of breeding values by combining genotyped and non-genotyped animals. Incompatibilities between the pedigree- and the genomics-based relationship matrices were addressed by fitting a covariate (J) that corrects for base-population differences. Alternative ways of combining the J covariate and genetic group effects were evaluated to account for missing parental information. Fitting J as fixed or random reduced biases and increased stability of genomic predictions. Combining group and J effects performed better than existing models. A model that fits group regression coefficients minus the part explained by pedigree was recommended for its least bias and highest stability.